SAN FRANCISCO, California, April ​9, 2025 – This week ​at the Google Cloud Next 2025 conference, Google took the packaging of its seventh generation ​Tensor ​Processing Unit (TPU), called ​Ironwood. It is not only a drop of chip in ​the technological ocean, it ​is a thunder, which ​indicates a crucial change in the design ​of the AI infrastructure. ​While AI developers have long been ​obsessed with ​the ​formation of larger and better models, Ironwood redirects the conversation to inference: the computational backbone of each interaction between users and AI systems.

According to ​Amin Vahdat, Vice President of Google and GM of ML, Systems and Cloud AI, Ironwood is designed ​to meet the requirements ​of what he calls the “age of inference.” ​It’s not just a brand, it’s a paradigm shift. For years he has focused on building massive fundamental models. But today, the real economic ​and operational challenges are to deploy these models quickly, economically and on a scale. Inference, not ​training, is the AI 2025 bottleneck, and ​Ironwood intends to erase it.

What is Google Ironwood Chip and why is it important?

Ironwood is ​Google’s most advanced AI chip to date, capable of a computer exaflops ​jaw 42.5 when ​implemented in a 9 216 chip capsule. ​Each ​individual smart watch ​in 4,614 teraflops. ​For comparison, this ​makes Ironwood 24 times more powerful than the captain, the fastest supercomputer in the world in early 2025, according to Google ​data shared at the conference.

The chip is also energy efficient: a critical metric such as global data centres contain energy restrictions. Google states that Ironwood offers twice the performance per watt compared ​to ​its predecessor, Trillium, and is ​almost 30 times more energy efficient than the ​first generation of Cloud TPU released in 2018. It’s not just the evolution, it’s the silicon ​infrastructure revolution.

“At a time when the energy available is one of the main limitations to the supply of AI capabilities, we offer significantly higher capacity per watt for customer workload,” ​said Vahdat. ​The emphasis on inference design reflects Google’s belief that the future of IV is not only thinking ​great, ​but ​thinking intelligent.

Why Inference Matters More Than Ever

Think of inference as the ​engine that improves ​AI experiences in real time. Training takes place once, but on a large scale. But inference is the constant rhythm of IV. This ​is what happens ​every time you ask a question in ChatGPT, whenever a ​recommendation engine serves you a product, or when an autonomous system makes a decision on the way.

According to Google, AI’s IT demand has ​increased ten times ​a year over the ​last eight years, leading to a cumulative increase of 100 million. Traditional flea ​architectures cannot keep up with this pace. Specialized equipment like Ironwood is not optional, it is ​essential. And it’s not just Google that says that. As VasureBeat points out, the Ironwood architecture was ​specially designed to deal with explosive growth ​in model size and frequency.

In the AI economy, ​inference is ​the most expensive and frequent operation. Efficiency translates ​directly into ​profitability and performance. This is the real bet behind Ironwood: restarting the infrastructure pile to ​support ​Ironwood’s smart, cheaper ​and faster deployments.

What are the specifications ​behind Ironwood’s lead?

The Ironwood chip has 192 GB of ​high bandwidth memory (HBM) – six times more than the previous generation of Trillium. ​Memory bandwidth is an incredible 7.2 ​terabit per ​second per chip, offering an increase of 4.5x compared to your predecessor. ​The chip also has an energy ​efficient design that allows companies to evaluate AI without increasing energy costs.

Although Google did not reveal the manufacturer behind Ironwood silicon, the ​company confirmed that Ironwood chips are firmly integrated ​into its own Gemini 2.5 ​AI models. ​This includes Gemini 2.5 Flash, a light version for daily applications, and Gemini 2.5 Pro ​for heavy use cases such as drug discovery ​and financial modelling.

Each Gemini variant benefits from ​Ironwood’s ability to ​dynamically adjust the depth of the reasoning ​according to the complexity of ​the ​pulse. It is not just about ​speed, it is about intelligence and adaptability. That’s what ​Google means when it comes to “thinking models.”

How Ironwood fits into Google’s biggest AI strategy?

Ironwood isn’t empty. It is the cornerstone of a vertically integrated ​infrastructure pile. With the chip, Google introduced its new WAN Cloud service – offering improved business network performance – and Pathways, a new Google DeepMind ML running time designed to evolve ​the model that serves through huge TPU ​networks.

“The ​main models ​of thought such as Gemini 2.5 and the Nobel Prize AlphaFold are now underway in TPU,” said Vahdat. And the test is not only in performance ​measures. Google’s $12 billion in cloud business, which saw a 30% increase in YoY in the fourth quarter of 2024, is ready to gain even more momentum from these investments.

While Amazon Trainium ​and Microsoft OpenAI The Azure platform poses serious competition, Google’s ​secret weapon is vertical integration. ​Unlike its rivals, Google designs its chips at home, optimizing through the software-hardware spectrum to provide maximum efficiency and cost control.

What makes Ironwood more than a chip?

Maybe the most ambitious part of Google’s announcement wasn’t even hardware. It was the revelation of a multi-agent ecosystem, ​anchored by a new agent development kit ​(ADK) and a first-class agent protocol. These allow AI officers, including ​those ​built in different frames or by different suppliers, to communicate and cooperate safely.

This interoperability ​is ​an important step ​towards breaking up the ​company silos that ​have historically limited AI’s potential. Google is already ​working with over 50 industry ​partners, including SAP, ServiceNow and Salesforce, to advance these open standards.

“2025 will be a transition year where the generative IV changes the answer to individual questions to solve complex problems through ​agent systems,” predicted Vahdat. Imagine a ​scenario where a financial intelligence officer detects risk, a compliance officer verifies compliance, and a communications ​officer develops an independent, real-time response. This ​is the vision that Ironwood is helping to achieve.

How will companies benefit from these advances?

For companies, Ironwood ​could be the expected solution to the AI dilemma. The cost, energy consumption and complexity of the system were the main obstacles to adoption. Ironwood’s heading ​for the three of us. According to ​Google, it ​reduces energy costs, reduces ​infrastructure overload ​and supports complex scale reasoning.

In addition, with tools such as WAN ​Cloud and Pathways, companies can now manage models through a distributed infrastructure ​with more control and fewer bottlenecks. As RTInsights pointed out, ​Google’s ecosystem ​also includes ​new partnerships and the integration of business ​products such as AMD, NVIDIA, DataChat, dbt Labs, DDN and more, all of which ​contribute to a real-time AI development environment.

With ​actual use cases ​already underway – from Wayfair using data agents to Lowe’s customer agents, the launch of Ironwood is more than just an ad. ​It is a market signal that ​AI’s business is growing, becoming smarter and ​more accessible.

What is ​the competitive landscape and what comes next?

The big question now is: How will competitors respond? Microsoft ​has already invested a lot in AI thanks to its OpenAI association. Amazon training chips are designed for ​similar use cases. The race to ​master the AI infrastructure is in, and Ironwood has put Google firmly ahead, at least for the moment.

But innovation is progressing rapidly. ​In the coming months, we will probably see counter-declarations, strategic alliances, and ​perhaps ​even normative battles over multi-agent protocols. What is clear is that Google’s bet on multi-agent inference and interoperability has ​increased bets for all in the AI game.

As AI moves ​from an experimental tool to a key business engine, companies will ​need ​not ​only powerful platforms, but also ​efficient, flexible and ready for the ​future. Ironwood checks all these boxes. And ​with the adoption of the ​real world accelerating, ​the success of ​the ​chip could set the tone ​for the next decade of AI development.

Google’s investment ​of millions of ​dollars in vertically integrated infrastructure, optimized for inferences, could be the most important moment of the company, but not only for its lower line, but for the future driven ​by AI helps ​to configure.